{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"10_PT_Data_and_Models","provenance":[{"file_id":"https://github.com/madewithml/basics/blob/master/notebooks/10_Data_and_models.ipynb","timestamp":1582855185698}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"ckRIiGksZUnw","colab_type":"text"},"source":["# Data and Models\n","In the subsequent lessons, we will continue to learn deep learning. But we've ignored a fundamental concept about data and modeling: quality and quantity."]},{"cell_type":"markdown","metadata":{"id":"K-BaYyztanFh","colab_type":"text"},"source":["<div align=\"left\">\n","<a href=\"https://github.com/madewithml/basics/blob/master/notebooks/10_Data_and_Models/10_PT_Data_and_Models.ipynb\" role=\"button\"><img class=\"notebook-badge-image\" src=\"https://img.shields.io/static/v1?label=&amp;message=View%20On%20GitHub&amp;color=586069&amp;logo=github&amp;labelColor=2f363d\"></a>&nbsp;\n","<a href=\"https://colab.research.google.com/github/madewithml/basics/blob/master/notebooks/10_Data_and_Models/10_PT_Data_and_Models.ipynb\"><img class=\"notebook-badge-image\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"qAE9BjMH8x4q","colab_type":"text"},"source":["In a nutshell, a machine learning model consumes input data and produces predictions. The quality of the predictions directly corresponds to the quality and quantity of data you train the model with; **garbage in, garbage out**. Check out this [article](https://venturebeat.com/2018/06/30/understanding-the-practical-applications-of-business-ai/) on where it makes sense to use AI and how to properly apply it."]},{"cell_type":"markdown","metadata":{"id":"iLYQQyDzFx5c","colab_type":"text"},"source":["We're going to go through all the concepts with concrete code examples and some synthesized data to train our models on. The task is to determine whether a tumor will be benign (harmless) or malignant (harmful) based on leukocyte (white blood cells) count and blood pressure. This is a synethic dataset that we created and has no clinical relevance."]},{"cell_type":"markdown","metadata":{"id":"LaHVAEnzKbKl","colab_type":"text"},"source":["# Full dataset"]},{"cell_type":"markdown","metadata":{"id":"1e1RSFdnKfN2","colab_type":"text"},"source":["We'll first train a model with the entire dataset. Later we'll remove a subset of the dataset and see the effect it has on our model."]},{"cell_type":"markdown","metadata":{"id":"rgbY9WkklG6T","colab_type":"text"},"source":["## Data"]},{"cell_type":"markdown","metadata":{"id":"FLH7kzZl8wnf","colab_type":"text"},"source":["### Load data"]},{"cell_type":"code","metadata":{"id":"5wDazzQdaoy2","colab_type":"code","colab":{}},"source":["import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","from pandas.plotting import scatter_matrix\n","import urllib"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zeI0jQj8CbAU","colab_type":"code","colab":{}},"source":["SEED = 1234\n","DATA_FILE = 'tumors.csv'"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NutCOofTCcNE","colab_type":"code","colab":{}},"source":["# Set seed for reproducibility\n","np.random.seed(SEED)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"GbsXoFVgdh6K","colab_type":"code","colab":{}},"source":["# Load data from GitHub to this notebook's local drive\n","url = \"https://raw.githubusercontent.com/madewithml/basics/master/data/tumors.csv\"\n","response = urllib.request.urlopen(url)\n","html = response.read()\n","with open(DATA_FILE, 'wb') as fp:\n","    fp.write(html)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"y6LNWmoidh8q","colab_type":"code","outputId":"4ff43d5b-523f-4cf9-a596-8214ccf2de6f","executionInfo":{"status":"ok","timestamp":1583944696012,"user_tz":420,"elapsed":6621,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# Raw data\n","df = pd.read_csv(DATA_FILE, header=0)\n","df.head()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>leukocyte_count</th>\n","      <th>blood_pressure</th>\n","      <th>tumor_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13.472969</td>\n","      <td>15.250393</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>10.805510</td>\n","      <td>14.109676</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>13.834053</td>\n","      <td>15.793920</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>9.572811</td>\n","      <td>17.873286</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7.633667</td>\n","      <td>16.598559</td>\n","      <td>malignant</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   leukocyte_count  blood_pressure tumor_class\n","0        13.472969       15.250393   malignant\n","1        10.805510       14.109676   malignant\n","2        13.834053       15.793920   malignant\n","3         9.572811       17.873286   malignant\n","4         7.633667       16.598559   malignant"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"4yUmtoznqc9r","colab_type":"code","colab":{}},"source":["# Define X and y\n","X = df[['leukocyte_count', 'blood_pressure']].values\n","y = df['tumor_class'].values"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nXFUmnfte6z6","colab_type":"code","outputId":"f1c2e39b-5256-4ac6-ac86-72013d763538","executionInfo":{"status":"ok","timestamp":1583944696467,"user_tz":420,"elapsed":7043,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":279}},"source":["# Plot data\n","colors = {'benign': 'red', 'malignant': 'blue'}\n","plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], s=25, edgecolors='k')\n","plt.xlabel('leukocyte count')\n","plt.ylabel('blood pressure')\n","plt.legend(['malignant ', 'benign'], loc=\"upper right\")\n","plt.show()"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhg6NRrFi5bBixYrHugYVlexQZ/wqLyUkMW7cRIwaNQnx8XGIiCiCbduO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nrniBzAdcjKQ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\nbOn7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OjrCmADamtlX4fhaoG2cNqnedgCXEnxii6emfZ8sV4XdApMTdFmkerv1ANaZ2bIE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\nnvidcy7DeOJ3zrkM44nfOecyzP8BfwdZPZ1K+7MAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"yVD-8qgGwz5l","colab_type":"text"},"source":["We want to choose features that have strong predictive signal for our task. If you want to improve performance, you need to continuously do feature engineering by collecting and adding new signals. So you may run into a new feature that has high correlation (orthogonal signal) with your existing features but it may still possess some unique signal to boost your predictive performance. "]},{"cell_type":"code","metadata":{"id":"kXXO2ZlGr8Kp","colab_type":"code","outputId":"e90bc4d8-a97c-4644-f55d-b6892559e391","executionInfo":{"status":"ok","timestamp":1583944696790,"user_tz":420,"elapsed":7312,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":429}},"source":["# Correlation matrix\n","scatter_matrix(df, figsize=(5, 5));\n","df.corr()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>leukocyte_count</th>\n","      <th>blood_pressure</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>leukocyte_count</th>\n","      <td>1.000000</td>\n","      <td>-0.162875</td>\n","    </tr>\n","    <tr>\n","      <th>blood_pressure</th>\n","      <td>-0.162875</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                 leukocyte_count  blood_pressure\n","leukocyte_count         1.000000       -0.162875\n","blood_pressure         -0.162875        1.000000"]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAUUAAAE+CAYAAAAakQJfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nols2umUzlqvz/MksPzudQxQE0lE/67tjPLS+k954gB8dy3A647bkHJkpA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Pj2/ml+676V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\nYkN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xV6TcaDKZqbC2I0DM72L3YIQ1HT5eHEmjaQr/dGSWXQNWq8yjGzvxuBx85ZWzqKqg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\npEYxkUgkMqRGMZFIJDL8BbBuZr+iGTU3AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 360x360 with 4 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"IcA13EL9MKq0","colab_type":"text"},"source":["### Split data"]},{"cell_type":"code","metadata":{"id":"v7vmMREZMK6w","colab_type":"code","colab":{}},"source":["import collections\n","from sklearn.model_selection import train_test_split"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7ZrZrhNvD5q9","colab_type":"code","colab":{}},"source":["TRAIN_SIZE = 0.70\n","VAL_SIZE = 0.15\n","TEST_SIZE = 0.15\n","SHUFFLE = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NklqFClX7p9H","colab_type":"code","colab":{}},"source":["def train_val_test_split(X, y, val_size, test_size, shuffle):\n","    \"\"\"Split data into train/val/test datasets.\n","    \"\"\"\n","    X_train, X_test, y_train, y_test = train_test_split(\n","        X, y, test_size=test_size, stratify=y, shuffle=shuffle)\n","    X_train, X_val, y_train, y_val = train_test_split(\n","        X_train, y_train, test_size=val_size, stratify=y_train, shuffle=shuffle)\n","    return X_train, X_val, X_test, y_train, y_val, y_test"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0pahDv9WLD2S","colab_type":"code","outputId":"eba3e6d2-93d1-42d9-e50f-a76395fc1176","executionInfo":{"status":"ok","timestamp":1583944697497,"user_tz":420,"elapsed":7909,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n","class_counts = dict(collections.Counter(y))\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")\n","print (f\"Classes: {class_counts}\")"],"execution_count":13,"outputs":[{"output_type":"stream","text":["X_train: (722, 2), y_train: (722,)\n","X_val: (128, 2), y_val: (128,)\n","X_test: (150, 2), y_test: (150,)\n","Sample point: [18.01865938 15.48133647] → benign\n","Classes: {'malignant': 611, 'benign': 389}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_OH53BpMi-e_","colab_type":"text"},"source":["### Label encoder"]},{"cell_type":"code","metadata":{"id":"oA7kC6GFjD8c","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import LabelEncoder"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wLC4GRM2jEAt","colab_type":"code","colab":{}},"source":["# Output vectorizer\n","y_tokenizer = LabelEncoder()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LMR1_Du9jEEf","colab_type":"code","outputId":"94affa25-b883-47b9-aeca-5ac422664623","executionInfo":{"status":"ok","timestamp":1583944697499,"user_tz":420,"elapsed":7838,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Fit on train data\n","y_tokenizer = y_tokenizer.fit(y_train)\n","classes = list(y_tokenizer.classes_)\n","print (f\"classes: {classes}\")"],"execution_count":16,"outputs":[{"output_type":"stream","text":["classes: ['benign', 'malignant']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"McIId6GajHty","colab_type":"code","outputId":"a00fd45a-e34c-4398-8789-c2325bced823","executionInfo":{"status":"ok","timestamp":1583944697499,"user_tz":420,"elapsed":7816,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Convert labels to tokens\n","print (f\"y_train[0]: {y_train[0]}\")\n","y_train = y_tokenizer.transform(y_train)\n","y_val = y_tokenizer.transform(y_val)\n","y_test = y_tokenizer.transform(y_test)\n","print (f\"y_train[0]: {y_train[0]}\")"],"execution_count":17,"outputs":[{"output_type":"stream","text":["y_train[0]: benign\n","y_train[0]: 0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"O7NWNJQxMM-t","colab_type":"code","outputId":"ac45dc4e-3e79-4bd8-e4df-d1794219e59e","executionInfo":{"status":"ok","timestamp":1583944697501,"user_tz":420,"elapsed":7796,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Class weights\n","counts = collections.Counter(y_train)\n","class_weights = {_class: 1.0/count for _class, count in counts.items()}\n","print (f\"class counts: {counts},\\nclass weights: {class_weights}\")"],"execution_count":18,"outputs":[{"output_type":"stream","text":["class counts: Counter({1: 441, 0: 281}),\n","class weights: {0: 0.0035587188612099642, 1: 0.0022675736961451248}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"rprTIVQIjLq6","colab_type":"text"},"source":["### Standardize data"]},{"cell_type":"code","metadata":{"id":"Vk-dk3X0jLxR","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import StandardScaler"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"GDTGSoERjL0B","colab_type":"code","colab":{}},"source":["# Standardize the data (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7CcSCO4bjL2n","colab_type":"code","colab":{}},"source":["# Apply scaler on training and test data (don't standardize outputs for classification)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hzQ5s5eqjPMy","colab_type":"code","outputId":"07baf721-df14-4d54-c568-7971054fb396","executionInfo":{"status":"ok","timestamp":1583944697503,"user_tz":420,"elapsed":7721,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":119}},"source":["# Check (means should be ~0 and std should be ~1)\n","print (f\"X_train[0]: mean: {np.mean(X_train[:, 0], axis=0):.1f}, std: {np.std(X_train[:, 0], axis=0):.1f}\")\n","print (f\"X_train[1]: mean: {np.mean(X_train[:, 1], axis=0):.1f}, std: {np.std(X_train[:, 1], axis=0):.1f}\")\n","print (f\"X_val[0]: mean: {np.mean(X_val[:, 0], axis=0):.1f}, std: {np.std(X_val[:, 0], axis=0):.1f}\")\n","print (f\"X_val[1]: mean: {np.mean(X_val[:, 1], axis=0):.1f}, std: {np.std(X_val[:, 1], axis=0):.1f}\")\n","print (f\"X_test[0]: mean: {np.mean(X_test[:, 0], axis=0):.1f}, std: {np.std(X_test[:, 0], axis=0):.1f}\")\n","print (f\"X_test[1]: mean: {np.mean(X_test[:, 1], axis=0):.1f}, std: {np.std(X_test[:, 1], axis=0):.1f}\")"],"execution_count":22,"outputs":[{"output_type":"stream","text":["X_train[0]: mean: 0.0, std: 1.0\n","X_train[1]: mean: -0.0, std: 1.0\n","X_val[0]: mean: 0.1, std: 1.0\n","X_val[1]: mean: 0.1, std: 1.0\n","X_test[0]: mean: -0.1, std: 1.0\n","X_test[1]: mean: -0.1, std: 1.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"YZwkrokjlL81","colab_type":"text"},"source":["## Modeling"]},{"cell_type":"code","metadata":{"id":"OSmsF8b8A_r-","colab_type":"code","colab":{}},"source":["import torch"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nF_-_GQTHMuL","colab_type":"code","outputId":"9260bcd7-361f-4689-986b-f68efa3288ed","executionInfo":{"status":"ok","timestamp":1583944699341,"user_tz":420,"elapsed":9517,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Set seed for reproducibility\n","torch.manual_seed(SEED)"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<torch._C.Generator at 0x7fbb1ac06090>"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"owLnzReJJdpj","colab_type":"text"},"source":["### Model"]},{"cell_type":"code","metadata":{"id":"F6PCHuW0rBco","colab_type":"code","colab":{}},"source":["from torch import nn\n","import torch.nn.functional as F\n","from torchsummary import summary"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6NiIKIzKHRMa","colab_type":"code","colab":{}},"source":["INPUT_DIM = 2 # X is 2-dimensional\n","HIDDEN_DIM = 100\n","NUM_CLASSES = 2"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pfDgN3F-GCKP","colab_type":"code","colab":{}},"source":["class MLP(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, num_classes):\n","        super(MLP, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, hidden_dim)\n","        self.fc2 = nn.Linear(hidden_dim, num_classes)\n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        z = F.relu(self.fc1(x_in)) # ReLU activaton function added!\n","        y_pred = self.fc2(z)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qaYgpFapGCRg","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":340},"outputId":"913f7e2b-4af8-4fcc-c0be-ae3cffc1d38e","executionInfo":{"status":"ok","timestamp":1583944738851,"user_tz":420,"elapsed":1007,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}}},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)\n","print (model.named_parameters)\n","summary(model, input_size=(INPUT_DIM,))"],"execution_count":28,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of MLP(\n","  (fc1): Linear(in_features=2, out_features=100, bias=True)\n","  (fc2): Linear(in_features=100, out_features=2, bias=True)\n",")>\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Linear-1                  [-1, 100]             300\n","            Linear-2                    [-1, 2]             202\n","================================================================\n","Total params: 502\n","Trainable params: 502\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.00\n","Forward/backward pass size (MB): 0.00\n","Params size (MB): 0.00\n","Estimated Total Size (MB): 0.00\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"fkSY4lj5Ibe0","colab_type":"text"},"source":["### Training"]},{"cell_type":"code","metadata":{"id":"bvAvgInrsXtp","colab_type":"code","colab":{}},"source":["from torch.optim import Adam"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_uzT5WdgrPcV","colab_type":"code","colab":{}},"source":["LEARNING_RATE = 1e-3\n","NUM_EPOCHS = 5\n","BATCH_SIZE = 32"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"w5zvcjQ8GCHF","colab_type":"code","colab":{}},"source":["# Loss\n","weights = torch.Tensor([class_weights[key] for key in sorted(class_weights.keys())])\n","loss_fn = nn.CrossEntropyLoss(weight=weights)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"q5t1LqwFrMej","colab_type":"code","colab":{}},"source":["# Accuracy\n","def accuracy_fn(y_pred, y_true):\n","    n_correct = torch.eq(y_pred, y_true).sum().item()\n","    accuracy = (n_correct / len(y_pred)) * 100\n","    return accuracy"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"CA3un62trOZi","colab_type":"code","colab":{}},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tEfrIX0tGCBU","colab_type":"code","colab":{}},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NXyUnTDlGB_f","colab_type":"code","outputId":"f265a00b-f16d-4afa-e98c-909d77a5be07","executionInfo":{"status":"ok","timestamp":1583944748938,"user_tz":420,"elapsed":1138,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# Training\n","for epoch in range(NUM_EPOCHS*10):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%10==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":37,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 0.70, accuracy: 48.8\n","Epoch: 10 | loss: 0.55, accuracy: 91.7\n","Epoch: 20 | loss: 0.43, accuracy: 97.6\n","Epoch: 30 | loss: 0.36, accuracy: 98.2\n","Epoch: 40 | loss: 0.30, accuracy: 98.2\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gp-2SuGlJCd8","colab_type":"text"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"id":"5MM9gNg6I4eG","colab_type":"code","colab":{}},"source":["import itertools\n","from sklearn.metrics import accuracy_score\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import confusion_matrix"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5sRAXVwPOSBs","colab_type":"code","colab":{}},"source":["def plot_multiclass_decision_boundary(model, X, y):\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n","    cmap = plt.cm.Spectral\n","    \n","    X_test = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).float()\n","    y_pred = model(X_test, apply_softmax=True)\n","    _, y_pred = y_pred.max(dim=1)\n","    y_pred = y_pred.reshape(xx.shape)\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"92T5F-83IxTe","colab_type":"code","outputId":"246355b1-a893-4075-b127-05c5324ff86f","executionInfo":{"status":"ok","timestamp":1583944751821,"user_tz":420,"elapsed":812,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predictions\n","pred_train = model(X_train, apply_softmax=True)\n","pred_test = model(X_test, apply_softmax=True)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = pred_train.max(dim=1)[1]\n","pred_test = pred_test.max(dim=1)[1]\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":40,"outputs":[{"output_type":"stream","text":["sample probability: tensor([0.3269, 0.6731], grad_fn=<SelectBackward>)\n","sample class: 1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"19TDTMr9eFkI","colab_type":"code","outputId":"0775da65-c7ab-4d33-833e-7799ca0e549b","executionInfo":{"status":"ok","timestamp":1583944752003,"user_tz":420,"elapsed":612,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Accuracy\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":41,"outputs":[{"output_type":"stream","text":["train acc: 0.98, test acc: 0.94\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ViwfNFOYRDkm","colab_type":"text"},"source":["We're going to plot a white point, which we know belongs to the malignant tumor class. Our well trained model here would accurately predict that it is indeed a malignant tumor!"]},{"cell_type":"code","metadata":{"id":"bzFb90SJOmI2","colab_type":"code","outputId":"0d68648d-5753-41e4-8138-c85c7af197fd","executionInfo":{"status":"ok","timestamp":1583944753389,"user_tz":420,"elapsed":1117,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(8,5))\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","\n","# Sample point near the decision boundary\n","mean_leukocyte_count, mean_blood_pressure = X_scaler.transform(\n","    [[np.mean(df.leukocyte_count), np.mean(df.blood_pressure)]])[0]\n","plt.scatter(mean_leukocyte_count+0.05, mean_blood_pressure-0.05, s=200, \n","            c='b', edgecolor='w', linewidth=2)\n","\n","# Annotate\n","plt.annotate('true: malignant,\\npred: malignant',\n","             color='white',\n","             xy=(mean_leukocyte_count, mean_blood_pressure),\n","             xytext=(0.4, 0.65),\n","             textcoords='figure fraction',\n","             fontsize=16,\n","             arrowprops=dict(facecolor='white', shrink=0.1))\n","plt.show()"],"execution_count":42,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAeIAAAE/CAYAAACaScBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nu7U018PYkKRhEJ7yEvH6kFJ1DrtaeTMjdhQ7CE6F04KxZhJZ2yM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mnvpqxk6mNxwRJhMl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WchM9lM4+Tex1EMNreLllOFS2gW4mlpxNOyMbPcNAwis4uk40lcDXU4fbv/wNP0\n0CCzH1zfuDytCRy1Hhx1Nbt+XKV6qUBcwVKxNLe+N0Iqms673ArZogmRpRj1XXubCR0UniY3FoeF\nTJ76xkA2XhiSyQszuBtcuHzOnC+p7fCiWbSc91zTBW0nynt8yV5jJ2+5KsDuKc4eJmSXabd6vLXk\nLIAv/1qAYUsNwS/96erecG/RxrIbRipNYHSKuD+IzevG19+F1ZX7s96OeGCF8TfPI01ztW63wNVQ\nS/dzj++qgEZdbyeZZJqla3eA7P6wu9lHx5mHdzU+pfqpQFzBbv9glEQoWTgBFECy6TJrKRlpg8DE\nCpmkgafZjbvBWfJKVKloCptrk0C8yjQlC7eW6H2qK+c+TdcYPtvPnR+OkY5nQGQvlm2nWva89LtX\nniYXdo+N+Epiw++FpgvaH24p2Tjuz5D+7QEXxtsXufyfJeUOwACpSJSRN97GNAykYSI0jeWbY/Q8\n9xju5u23G4Tsz338rQsYqY1nfWNLQRau3Kb14aM7Hp8Qgqaj/TQc6SEViWGx23ZVLCTuXyE0PY8Q\ngtrutmyymVKVKuMKruSIBxPZZcgtcrF0m46nqfzdVkJzEe78cAwA0zTRNA1Pk4vBT/SW7OxtJpnh\n2nfubBmEAZCsH/HJx1nr4MRnh4kHExgpA5fPiW4tf/lAIQRDL/czem6S8Fy2KpXQNTofbSv5qsiZ\nsxFeO1ZP5s3XufafKydpcPr8lQ2BU5rZlY3Jty8y/JkXd1SuMrbkz8lwXnvMwMjkrgLxGk3Xd5Uh\nLaVk5sIVViZms2MTsHRzhMbhfppPHNn1eJTyUYG4QqXj2TOdFCjOIHSBbtUZerGv7PWPzYzJnTfH\nNh4ZMk3CC1Hmri7Sfqo0M7WF236MdO5FMx9NF9S0bX4RFCJ7NrfSWB0Whl7sI5PMkEkZ2Ff3tBUw\nMwaxpUDe+6RhkAiu4PQV7t/8oAdnwg8+VzmEZxbuBWFY3W4xWbo5gre9eU/710p5qEBcoZz1zoIV\nkix2nd6nu6ht91bEBXhlJpx35i4NyeLt5ZIF4vBseEPrwoJEdiWhacC3/4PaRxa7pWK2JarFTk/7\nORvqCmbQOxu2H9CLKTAymX+Wbpgs3RqF1TPJNreLxmMDeFp2thyvlJ46R1yhrA4LjYP1OWc6hS7o\nPtNBXWdNRQRhyO4NF7rCFUoy2w+bZY476+zoVg3NqtHQV8/xTx9B3+E520qTSWbwjwcJTKxseyXg\noNMseuEZodBw1u8sK9nqdFA/0JXnbLFO2+ndL0vvxWYz8dDkHKGpOdLRONGFZSZ+/D7+uxMlHJ2y\nG+rjdAXrfqIDm9vG/PUlMskMDq+dzkfbqOusrCMO3hZPwa1sb0vpEkiajzYSnFzJWUkQumDg+V4c\nm5RnrDbzN5eY+mD23raElPQ81VnW7lAJU/DNQAPnIrWkpOCoI84v+RbpsJX2OFP74ycY/f47SMPM\n7g8LEJpG55mTCG3nc4/W08dw1HlZvjlKJpHC2VBH84mhHQf1YqnpaiXuD+b2I4acD8TSMJi/dIO6\n3g7VIrGCqUBcwYQQtD3UTNtDldvxB7LHZhr66/GPBu/NgEU2+7jjkdIVgvA0uuh4tI2p92fvrRZI\nSc/TnQcqCEeWYkx/MIs0JPK+j0Bj70zhbnDhqNnf13rv3LCJTGVbL5oS/t1cF1MpG5nVhbYrCRf/\n+2wX/0v7BM3W3XUY2g1HrZfBV57Df2ec+PIKNq+LhiO9u84qFkJQ39dFfV9uhn051Pd2Erg7QSoS\nuxeMNQ3MQqtPgkQwhKuhclt4HnYqECtF0fNkB55G1/rs3dvqof1US8kDYMtwIw29dYTmIgghqGnz\nVES2czEt3FzKmz8gTcniHT9dj7bty/Pma+oQ/C+jTM71cj3hYjZtXQ/CWYKU1PiboI9/1rS98qLF\nYnU6aDm582Ie1UCz6PS/9DT+u5OsjE+DplHX087cxRt5t4iklGgWdamvZOqnoxSFEILGAR+NFZAA\nZbFb8PWUJ5FmN6QpWZkNE12KYXNZ8fXVoVsKf3hIFzp2JTe5b4/WZsGFCnfcTTpIydxlXxPBzWTl\nZZ5XO81ioXG4j8bhvvXbIvPLRGYXc4Kx1WlXZ4wrnArEilJGyUiK63+/8ezz+LvTdD3eTsvRxrzf\nU9PmJbIUy8kQ1yzZFYBim4+HOHM2yqu9KYYuXSP4VzeZnOvdOCbdwCZMkjL3A0Sdvo1z3cqedazu\njWcSScyMgWbREZpG17OPlv2Io7I5FYgVpYzu/HAsbwGSyQszOGvtec86Nx3xMX9jiYyZWT82JjSw\nOKxlWwl40hXm6/6mnNttwuSVmvznepXisjjsDL7yHOHZRRLBMDa3k5rOVrRNVleUyqCOLylKmSRC\nyWwJ0wJmLuffV7XYLRz/9BF8vXVoFg3dqtHQ7+P4pwa3bFW4G6Y0AHPThg4u3eQ3WqZxaQYOkf2f\nFZNXavw86o4WfUwHlZkxCE3NERyfJh0v/LtRiNA0ajpaaH5oMJsprYJwVVAzYkUpk0xq87O/yXDh\nYz82l5X+Z7uLPaQN7m/q8GpPOrs3/J3RnGXpNcOOOL/XdZfrcRcJqTHsiFOjq/PN2xWeXWTq7Q+B\n1YUOU9J4VJWtPAxUIFaUMnHWOTa/v37z+/fTWnLWmZdD/Page9tNHSwCTrpipRjigZJJJJk890HO\n2eClm6M4fbV42yv7CKOyNyoQK0qZ6BaNjtMtTH0wl3Of0KDjVOnOYN8vm5wV5tXeDIPvfbh+REnZ\nPysTM3lvl4bB8q2xsgZiaUrCswvEFv3oDjt1Pe1YneX7kHgQqUCsHFpGxiQwFiQWTOCstePrrSv5\nmePW481YbDoTF2bXi6FYnBb6nu7C3VjerlpHnY1kQAXhEkjHk/krZZGdLZdCKhojHUtg97rX2zIa\n6QxjP3iHVCSGmTEQmmDx6m26nn6kKmbp6XiS8PQc0pR42pqwe93lHlJeKhArh1IinOTG39/BNCRm\nxkSzCKY+nOPoJwdw1pb2037jYAONgw0YaQPTkFjsujpucsi4m3wE7k7k1pEWAvc+N20wUmkmz31I\nbCmA0DWkYVLb0077Yw+xePU2yVB0vZVktgGGZPLtixz92RcrulBIYGSS2Q+ugQAkzF++SX1/F62n\nj1Xc35fKmlYOpZEfT5BJGuuzUDMjMVIGIz8qX4F83apjdVjKdpGYj4eYifiJTIR4748j/O5/WODG\nzMEpDVrJPK1N2DzunFrYmkWncbh/X5978tyHRJf8SNPETGeQpsnKxAzzl28RHJteD8L3EwIic0v7\nOq69SEWizH54DWma6zXHpWESGJkiMrtQ7uHlqNyPM4qyT9KJDPFAIu99iXCSZDSF3W0r8ajKazYW\nwMwYxD+cI75oEkjAN0nx16KTn61d5tN16izwfhKaoPeFMyxevb0e/DytTbScGsLq2t0KTSoSIzA6\nSSaexNPaiLejFU3fGOhT0Vi2f7P5YLMIk+WbowWbZEjAzNOKsVJk38M85T4Ng+XbE3jbS9OadbtU\nIFYOHdPIduTJRwiQJWzdWAnWMqQ/VrfIt+dN1o4Km2ikJPzVSgNPusM0WndeIeuDmJu/W/ERyFgY\ntMf5bJ2/5N2YqkE8sMLS9RESK2HczQ00Hu0v3M5xG4ITM8ycv5wNRlKyMjWH9eod+l96Gt12r11o\nOpbILkcXaBhR6HZMibs5f+W3SpBJpQu2ZjVSpWtAsl1qaVo5dGwuK1ZH/s+gulXHvs/diyrRV74Q\n5d3v3QvC95MSPojtvHTm3wR9fHWxjbtJJ37DyvmYl/9ttpvR5OF7fzcTnl1k9PvvEJqaIxWOEpqa\nY/QH7xCe2d2b/+7cAAAgAElEQVQSqpFOZ4OwYa4HI5kxSEdjLFy5veFr7V53wSSxDe7bLhG6TuPR\nfqzOyv05elubEHmKmWQLnlRekpkKxMqhI4Sg96lONF3cmxkL0HRBz5nOikvkKJU8K3nrNrkrr6ih\n8Tcrvg2NICSCpNT4r/7SXQgzyRSL1+4w/tZ5Zj+4RjIUKdlzb4eUkpkLl3OCoTRMZi5cQRaY1W0m\nMruU93dYmjLnmJTFYae2px2hbxIKhMDVWIe91ou7pZGuZx6p+CIjntYmHLXeja9LE+h2G77BnvIN\nrAC1NK0cSjVtXo596gizVxeIBxI46+y0Hm/G5Tu8nYJeecHJf/mvK6QeaNwgBJx27axM5Z2kEwuS\nfIuAd5MOpNwwySKTSJIMRbA4HUU7YpIMRxn93tuYhpENdGKZwOgknU+dpqaj8B6hkUozf/kmKxOz\nILPHXlpOHcXm3tnvRjIUITK/jG614G1v3rAkvCYdjRdcKjXSGVKR2I7fDynNgh+c8gX29sceQrdZ\nWb45mvd7NF2jvq+Lut6OHY2jnIQm6P3EkyzfGiM4OoVpmtR2ttJ4bCDvz6HcVCBWDi1nnWPfy0RW\nk3/yOQ/f/eY880nn6kxWYhOSs94ALdad7as5tcLBwCrkehCWpmT2g6sEx6bX9yoddTV0P/vo+lnW\n3Zp9/+rGICcl0pBMv/cRns++lJO4BNn8gZHvvU0qEltf1g1NzhGdX2bwlee2NabsLPfKvdmnEPD+\nlbxnb4WuF15ukBJN3/m5dk9LY/7lDUHes79C02h9+ChWtzPb0zjPvrB3kw8ulUrTdZqODdB0bKDc\nQ9mSWppWKpqUkuWRAFe+fZMP//wKN757h/CCaiKwHzxujT88+Ta/VL/IQ44oT7rC/EbzNL/gW97x\nYw3a49hEbjCwYPKMO7T+78XrdwiOT987OmOYxP0rTPz4/T29FmmaRBf9Be6EuD+Y967w9ByZeCIn\n0cfMZFi+M76t514Zn2FlYjZ7bMYwkZnsjHzy7Q/JPLAJb3XacdTldtgCsNd6dpUxbXHYaTpxJBvk\nVwlNQ7fZaDk5XPD7fAPd1Ha1InQNoWtoFh3NotP17GPo1vLM2TKJJOHZReKBlV0t01cLNSNWKtrM\nR/PMX1vEXO29G1mMcet7Iww+30tte/4LmLI92aYOBiCRRgopHDh0kxdqVnihZmVPjy0zGX41/T5/\nJB7BFBoZoWMVJi3WNL/oy54/lVKyfGssN1lIShIrYRIrYRy1+/EzLnxBjy74c4tqkJ25R+eW4MTQ\nlo++fHsMWeBoT2hyDt/gxlWYzqceZuR77yAN414fYV2n86nTWz5XIU1H+3E11OG/PUY6nsTT2oRv\nsBuLvfCxPCEEnWceJnlsgOiiH91qxdveVJaiHVJK5j68TmBkEqFpSCmxuhz0PPc4Nk95K87tBxWI\nlYqVSRnMXVtEGg+ecZRMXJjh5GcLf7pXNrfbpg7bkYrGGXnjHNaMweflBW43DBG1uXl4oJ5HWvR7\ne8NSYqbzH4kSmkY6lth1IBaahrvZR3RhOTfuCoGrIX/fZovTDprIu7Rr2WZ95UJ7vtIw895n87gZ\n+ulPEJqaIxmKYPO6qe1q23MLQ3eTD3eTb8ffZ6/xYK/ZeZZ8MflvjxMYncoW4lhdKk+Fo4y9+R5H\nPv38gUuoVIH4kIsF4izcWCIRSuJuctNytBGbqzKSGWL+OJomMIzci2IynMQ0zLz7fEpha60Nz5wN\n82pPhsHzF4ve1GHm/GWMVAok2ICHFq8BYFm2w2deYC1VXWgaVpeDdCy3uIo0TBy1ewsG7Y+dYOSN\nc/clawmEJuh86uGChSrqejtZujGCfCB6C12nYah3W8/raW0kMDKVs7wtdB13c/7AqFn0qkqG2m9L\nN0fzrioYyRSxpcCuPmBUMhWIDzH/eJCxc5OYpgQJ0eU4i7eXOfbJwS1b9JWCxa4X3BcSmjhwn4pL\n5czZCK8dqyfz5utcK9IseI2ZMbJ7s3l+bGYmQyIYwll/r1BF88nhnOM7QtfwdrRgde0tg93mcXHk\n088TGJkkthTA5nHjG+zC5imchWxzO+k48zDT7360IaGs+cTgti/+TccGCE3OYaTT6++D0DVcTfU4\nC8zElY2MfAfaV+X74Fbt9hSIhRCfA/5n4BjwpJTyQjEGpew/0zAZf2dqfe8VshccaUrG3p3i2CcH\nyzi6LGedA5vLRiK0sfuM0AQNfXUITQXiyrN5Qs2DlZrqetoByfxHN8kkUghNwzfYTcvJrfdit0O3\nWWk8urNazbWdrXhbG4nMLSFNibulYdO91QdZXU76zz7LwtXbRGYX0Sw6voFuGoZ61YfHbbLXekgE\nQjm3Sylx1teUYUT7a68z4ivAPwK+WoSxKCUU88cLXjKjS7GKWPYVQjD4Qi83Xx/BSBlIKRGAy+ek\n63G1jLdT9ydnkd6fMpOaxYKjzpv3IooQG2bDa+p6OqjtbkcaBkKvjM5TmsVCTefu+0Hb3E46nzxV\nxBEdLi2nhpn48fs5KyXulsay71/vhz0FYinldaAi/nCUHRJi5+WSysDhtXPq544SmouQiqZw1TvL\n3qe3Gt2fnPVqj0Hm3JuEvzNKMZel17Q/fpKxH7yDaZrZpCeR3Q9uf/xEwb1ZIQTCYiEdT7J8a4zo\nwhIWh52God7suVjlUPG0NNL97KPMXrxOKhRFs+jU93fRXKSVkkqj9ogPKbfPiaYLzAeTVgXUtHrK\nPhu+n9CEOqq0S/fPgr/8awGGLTUEv/SnXJvrZT+CMICzvoaBTz7H8q0x4v4gNo+LxuE+HHWbLymm\nojFGXj+HmTHWl7CjC36ajpe3KIOUkuDYNP4745jpDJ625oqvtXwQeFqbOPJKU7ZxhTjYE74tA7EQ\n4g0g3xrNa1LKv97uEwkhPg98HsBTX31VWg4aoQn6n+vhzg/H1veGNV2gWXV6znSWe3hKkZjS4MzZ\nMK8NusicK94Rpa3Y3E7aHjm2o++Zv3Qz53iPNAwWr96hvq9zz5W2dmv63UuEphfWs3j9d8dZmZhh\n4KeexbrNI03K7h2GXJAtA7GU8uViPJGU8mvA1wCauo9WwaLowVfT6uHEZ4dZuusnEUriaXTR0F+P\nbt3b+UWlsvxCf/ZCtl9L0cUSmVvMe7vQBNGFZWq720s8IkgEQ4Sm5zcWHTElRirN4vW7tD/6UMnH\npBw8amn6kLO5rLSfVCsUSgXYZOmx0N7yfovML+c/QiclkZkFUIFYKYI9/XYLIX5eCDEFPA38rRDi\nu8UZlqIoezEfDzEb83PmbAhppMice7PcQ8pLSomRTiNNSV1Pe95lSCmzRTLKQbMUzuLea+UrRVmz\n16zpvwT+skhjUbZJmpKoPw5kk64Owx6Ksj33J2edeTnEbw+4Md5+q+iFO/ZKSkng7gQLV25jpDNo\nukZdbydWt4tMPIGZMbKzYAFdTz9clnrHADWdrcxdvJ5zu9B16gdU5y6lONTSdJVZmQkz8pOJ9frL\nQhf0P9utsoqV9SB85mxo38pXbkWaJpG5RdKxJE5fLU5f7rlhAP+dCeY/urmeAGVmDPx3xkHTqO/t\nACGwOh3U9bbvucLWXljsNjqeOMX0+Y9AZl/fWqlK332BOJNMsXxrjMjsArrNhu9ID9725gOd6asU\njwrEVSQRTnL3zbEN1bDIwN03xzj+M0M4vOo4xWF35myUV3sNhi5dY+U7pQ3CiZUwYz98D2kY6/uq\nTl8dPc89vmEZV0rJ4tXb+TsUmSaBsWnaHjm2IdCVU213G+5mHyuTs5jpDO7mBpwNdetBNh1PMvL6\njzFS2SV2gNhykPqBLtpO7yxzXDmcKuewqLKlxVvL2brQDzBNyeKtnfeMVZRikVIy/qMLGMlU9hzw\nai/e+HKQ+cu3NnytkUrnbTW4zjRZuHKrovrPWhx2Go700nR8EFdj/YaZ7uK122SS94IwZI9dBe5M\nkIqo3tmVSEpZWb9f5R6Asn2JUDJ/NSxJTj1mRSml+HIQM0+LP2maBEcnN5wp1q2WTTOkAcx0BiOV\n3lGN53IJTy/kdFoCQEB4domGI4WbTCilFfcHmf3wOvHlIELXqO1up/X0UXRreTvOqUBcRdxNbkJz\nkZz+vEIXuJvUH7tSPkYqXTC4mpnVOuHiXvvD+oEuAncmcppArBMiG7DzkFKyfGuMpRsjGMkUNo+L\nllPDe6oNvReFj1aJfalQJ02That31it92Ws9tD58rGyZ5dUisRJm9AfvrW+JSMNkZXyaRCBE/9ln\nyrqfr5amq0jToC/vH7amazQNHqz+nEp1cfpqNxa9uI+jribnItd6apiarra8Xy90jfr+roIBbv7S\njWy29WqrvFQkxtS7lwhOzOzhFexefX8nIl/AlRJvR/HP6E+9e4nlW6OY6Wx92uRKhImfvE9kfqno\nz3WQLF67k5OXIE1JMhIlulDerT0ViKuI1WHh2CcH8DS7s73VBXia3Rz75ABWh1rcOMyyGdMZwGBY\n92ImUyVN1LI47PiO9CD0jWdrha7RmqfUpdA0Os+cYvCV57DXekETaFYLQsv2Im45dTTv82SSKfx3\nJnIvqIbJ/KUbZdn3a1ito72ekKYJhK7R/sSJoi+tpyIxwtMLOR96sq//ZlGf66CJLQXz3i4Ng7h/\npcSj2UhdvauMo9bB0Z8awMxk/xA1i/osdZgVaupQyiC8puXUMPYaN0s3RskksseXmk8M4WqoK/g9\n9hoPg5/8GMlwlHQsjt3rweoqXL85GYogdC3vknYmkU0UK7SkvR3xwArJUBS714WjvnZby5WartP3\n4lNE5paIzC9hsVmp7enA5i7+sat4YAWhCWSexYfESrjoz3eQWF0OMvFEzu2arpe9ZrgKxFVKBWDl\n/taGvz3oxnh7f5s6ZJIpwjPZ2ZinrRGbe2M7SiEE9X1d1Pd17fix7V43du/WeQ4Wh73gvrLQdr8n\na6TSjL91nsRKJNshVGbH1PP8E9ua1Qoh8LY14W1r2tXzb9dmjS90W3kTjipd49F+pt65lHtsTghq\nOrfeQjAzBuHZBYxUGneTr6h9kVUgVpQq9pUvRBnWPAR+50+2NQvOSLgY8zCXttFiTfGIK4JlGzkq\nwfEZZi5cJrsnIuEi+I700HJquKRJLnavG0etl3hgZcMJAqFr1Pd17rom9fT5j4gHQ2DK9YdNrISZ\neucivc8/ufeBF4mrsR7dbsPMxDfcLnSNhqHe8gyqStR0tNB0rJ/Fa3eze/pSolksdH/ssS0rt0UX\n/Uz86AKQTRZEQk1XKx1PnirK778KxIqyD6SU+MeCzF9fIp3I4G1x036yBUdN+Yqu+DMW/u1sFzFT\nIyk17MLErjXzWusETdYHG1Pfk4rGmblwOWdf0n9nAneTD297834PfYOuZx9j/EfnSYVjCCGQpomn\npZGWh/PvK2/FSKWJzC7Bg2f0pSS2GCCTSJatBeODhBD0Pv8kY2+dx0gkQQikYVLb3U7jcH+5h1fx\nmo4P4hvsIbYcQLNYcs6E52NmMkz86ELO2ffQ1BzOhjoaBnv2PC4ViBVlH0y9P8vi7eX1Kmj+sSDB\nyRDHXhnEWbf3/aj5eAiQSDONFNt7vK8tthI0dMzVHM2E1Ekakj9cbONL7ZMFvy84Np03CUoaBsu3\nx0seiK1OOwNnnyURDJGOxnHUebF5dn98z0gXPnolNEEmmaqYQAxg87g48qmPE/evZPfi62vKWga0\n2ug2K9627f/OhqYX8t4uDRP/7TEViBWlEqViaRZuLW+otIQEM2My9eEsR17o2/VjP9jUYVj3Yrz9\n1pbL0hFD427SsR6E7w1LMJW2489Y8Fnyz4qNVCp3trh23+oRolITQuCsr8VZn7+W9U5YnQ40XcPI\nV3KTbOCrNEKITZPglOK5v3Rp7n2FV5J2QgViRSmy8HxkNbM19483PBfZ9ePen5z1aq+xo6YOSall\nJ315ricakDAL7616WhoJjk7lLM0JTeDZ5+SkUhCaRvPJIeYuXt+w/C50naaHjqDpqt3hYeZuqs+m\nRjxIgLupOPUbVCBWlCLTrYUv3LvNds8uRcOX/3mQo5YaAr/zJ1yb62W7GdL1ega3ZhA0cp/fKkxa\nrYVntp7WJmw1HpLB8L2MZSHQrNZ9SxCSUhL3ryANA6evdt/bIPoGutFtVhau3CYdjWN1OWh6aJC6\nno59fV6l8jnqavC0NhGZW9zwQU3TdZpPDhXlOVQgVpQiq2nz5N1yFLqgcQ8V0M6czTYQMN9/e8ff\nqwn4lYYF/nCxjZRcrQaDxCYk/41vgc1aWgtN0PeJMyzeGCE4OoU0zWwG6kOD+1ILOu5fYeIn769W\njhJIKWl9eBhfEfbiNlPb1UZtgWpfyuHW9fRplm+N4b8zgZHO4G720XJyaFtH7rZDBWJFKTJN1xh8\noY/b3x8FJKYh0XQNl89J+6nilzzcrtOuKL/ZMsW3V3xMp+y0WVN8ts7PkCO+5fdqFp2WE0doOXFk\nX8dopNOMvfneevnGNXOXbmCv8eBubtjX51eUfISm0Xi0n8aj+5OZrgKxouwDb7Obh189RmBihXQi\ng6fJjafJtaszh2t7w8hMNjlrD+UrBx0J/pWjPDWZt2NlYi5vwQ5pmCxeH1GBWDmQVCBWlH2iW3Ua\nB3a/FL1WP/rM2TCv9mR2lJxVrdKxeMHmEenY1jN3RalGKhArSgXaUL5ywI3x9ltc28fylZXCWZ9t\nnvBghjZC4PTt/aiSolQiFYgVpULttHzlQeBtb8bisJOKxrMFn1cJTaPp2EAZR6Yo+0d1DlAUpWII\nTaPvpaep6WhZr3blqK+h74Uni1pkX1EqiZoRK0qF2U35yoPEYrfR9cwjSFMipakKaigHngrEilIh\ncnoL6+XrLVwJhCYQqCCsHHwqECtKBSh1b2FFUSqHCsSKUmbz8RBnzkZ5tTfF0KWrBL9089DOghXl\nMFKBWFGUA0eaJovX72ZLEqbSOOq8tD58VBUEUSqSyppWFOXAmXr3I5ZujGTbNEpJIhBi/EcXiC76\nyz00RcmhArGiKAdKKhIlPD2fU6FLGibzl26UaVSKUpgKxIqi7EomkSQRDOdWwSqzuH8FUaCdVCIY\nLvFoFGVrao9YUcose2TJBCkxk4X7AlcKI5Vm6t1LROeXEZpASmg82k/T8YFdNbUoNovDXvA+3WYt\n4UgUZXtUIFaUMllr6gDwak+aYUsNwe9UflOHiR+/T8wfBFMiV1d/l26MYLFb971n8Ha4mnxoVmvO\nTF3oGr4j+ccnpayIDxHK4aQCsaKU2P2FO6rt3HAyFCEeWAFTbrhdGgaL1+5WRCAWQtD7/BOMvXUe\nM5UGQJqSms5WGofv9ZM10hnmP7pJcGwaaRg4G+poPX0MV0NduYauHFIqECtKGZw5G+bV3gyD731Y\nVa0Nk+EoQtPytirMJJKYpommlT/1xF7jYeinP0FsKUAmkcTpq8Pmdq7fL6Vk/M33SATD6/2P48tB\nxn74Hv0vPYWjrqZcQ1cOofL/xSjKIXXU2QhQNUEYsgHONAonZ/lvj5dwNJsTQuBu8lHb1bYhCAPE\nlgIkQpH1ILxGGgYLV2+XcpiKogKxoijbZ/e6N+2C5L89VrrB7EHcv5IThNfvW14p8WiUw04FYkUp\nkfl4iNlYAFNmeLUnjUwlCX9ntNzD2rH6/u71FoUPMlb3ZCud1WlHFFhCtzgLZ10ryn5Qe8SKUgIH\nqamDp9mXPbZkyJz7nL7qSHTytrcgxDUkD2ZW6zQe7S/wXYqyP1QgVpR9tJYhfeZsiFd7MtkjSlXe\n2tBe48HT1kRkdnFD0pbQNVpODZdxZNunWXR6P/EEEz96HzOTAQTSNGkc7qOms7Xcw1MOGRWIFWWf\nZTsrGQxdukbwrw5GZ6Wup06zeGME/+1xzHQGZ0MtLaeO4vTVlnto2+asr2XoMy8QWwqsvoY6LHZb\nuYelHEIqECuKsmNC02g+Pkjz8cFyD2VP1jKrFaWcVLKWouyjaitfqShK6akZsaLsg7XylWfOhnm1\nJ8Pg+YuEq6B8paIopacCsaIU0f3lK7/8a4H15Kxrc71UY4b0boSm51m4fItUNIbV6aDpoUHqejrK\nPaw9k1IiDROha6outVJUKhArSpGdORvmtWN1ZN6s3iNKuxUYnWL2g6vr2dSpSIyZC1fIxJNVeyxI\nSkng7gQLV+9gpNJoFp2Gob6K6TalVD+1R6woSlFIUzJ/6UZOHWppmCxeu7NpacxK5r89ztylmxjJ\nVHavP51h6cYIcxevl3toygGhArGiKEWRSSQ2DbapcLSEoykOaUoWrt5BPvC6pGEQuDtZNZXElMq2\np0AshPiKEOKGEOIjIcRfCiGqo6yOohTZQSlfuRea1Qoyt9oWZAOaXoVndI1UKicIrxGaRipSfR8u\nlMqz1xnx68AJKeUp4BbwW3sfkqJUl7UAfOblFf76iwaD5y9y+fNvHroMad1qwdPWjNAe2DcVAmdD\nHVanozwD2wPNaoEC28DSNLFU4WtSKs+ekrWklP9w3z/fAX5hb8NRlOrxYPnKwfMXq6q38H7oeOIk\n42+dJ7ESQYjsBNnmcdL19OlyD21XNF2nrreT4NjUxnKemoa72VeVHy6UylPMrOlfBf680J1CiM8D\nnwfw1LcU8WkVpXzuL1+5os4Jo9us9L/8DHH/CslQBJvHhbOhrqqzi1tPH8VIpQlPzyN0DWmauBrq\n6XyqOj9cKJVny0AshHgDyFcF/TUp5V+vfs1rQAb4s0KPI6X8GvA1gKbuo/k3khRFORCcvtqqqju9\nGU3X6Xr6NOl4gmQois3txOZxlXtYygGyZSCWUr682f1CiH8K/AzwkpQFMjUU5YC5V7jDOPTlK5Oh\nCHMXrxNd8CM0jdqedlpODaFbreUeWlFZnQ61FK3siz0tTQshXgH+DfC8lDJWnCEpSmVT5SvvSUXj\njLzx9morwWwCU3B0kthSgIGzz+YmbimKkmOve8S/D9iB11f3gN6RUn5xz6NSlAqkylfmWrp+N+fs\nsDQl6WiMyNwi3vbmMo1MUarHXrOmq7sHmqLs0GEuX5lPdNGf9+ywmTGILQdVIFaUbVCVtRRF2bVC\ne6ZC17A47CUejaJUJxWIFWWbssvSQPrwJmY9qGG4D6Hree4R1Ha3lXw8ilKNVCBWlC1ky1f6AZkt\nXynloStfWYi3rYmmY/0ITUOz6GgWC5rVQs9zj2GpwpKWilIOqg2iohRwf3LWmZdD/PagG+NttTf8\noKbjg9QPdBNb9CN0HXezDy3vLFlRlHxUIFaUArLlK8O82pNW5Su3YLHbqOnMV/dHUZStqKXpKpUI\nJQnPR8gkM+UeyoH2C/2Co9baQ3tOWFGU/admxFUmFUtz580xEsEEQhOYhqR5qIHOx9qqup6voijK\nYaUCcRWRUnL7+6PEVxIgASN7fnPx9jI2t5WWY03lHeABcf/esDRSSKHKGiqKsn9UIK4iMX+cZCSV\nDcL3MQ3J3LVFFYiLYDYWQCVnKYpSSioQV5FUNF3wvnRC7RXvRb760So5S1GUUlCBuIo46x0UanBl\n96oqRnt15myE147Vk3nzda6pWbBSIolgiODYDGYmg7ejGU9rk8r3OGRUIK4iDq+dmjYPodkI0rgX\nkIUu6HxEHR1RDhcjlSYyt4g0JZ7Wxqosqbl4/S6L1+4gDROAlYkZnL46ej7+OEJTh1oOCxWIq8zA\ncz1Mvj/L0l0/0pRYnVY6H22lvutgNGEvF1W+sroEx2eYuXB5feYoTUnTQ4M0HRso88i2LxmObgjC\nsNYsI0BgZBLfYE8ZR6eUkgrEVUbTNXqe7KD78XZMw0SzaGoZaw8e3BvOnHtztXxlb7mHphSQikSZ\nuXAZaZgb8hYXr93F1ViPu8lXtrHtxMrkLNLM3WqShklgdEoF4kNEBeIqJTSBrqkygrulegtXr8Do\nVIEAZuC/PVY1gRhT5m0hCSBNM+/tysGkArFyKK2Vr3xt0EXmnDqiVE0y8WTBAJZJVM/Wgre9maWb\nIxuWpgGEplHb3V6mUSnloLIBlEPrF/qzS/qqk1J18bQ2Iiy5q0FC0/C0NpZhRLvj9NVS29W2oY2k\n0DVsHqdalj5k1IxYUZSq4u1oxXbtDqlI7N4StQDNaqF+oLu8g9uh9idO4u1oIXB3AjNjUNPVSn1f\nJ5pFXZoPE/XTVg4VVb6y+mm6Rt+LT7Nw9TYrE7NI08Tb0ULLyaGq64EshKCmo4WajpZyD0UpIxWI\nlUNDla88OHSblbZHjtP2yPFyD0VR9kwFYuXAW5sFnzkbUuUrFUWpOKJQycR9fVIhFoHxkj9xZWgE\nlso9iENAvc+lod7n/afe49LY7/e5R0qZtzNPWQLxYSaEuCClfLzc4zjo1PtcGup93n/qPS6Ncr7P\n6viSoiiKopSRCsSKoiiKUkYqEJfe18o9gENCvc+lod7n/afe49Io2/us9ogVRVEUpYzUjFhRFEVR\nykgF4jIQQnxFCHFDCPGREOIvhRB15R7TQSSE+JwQ4qoQwhRCqKzTIhJCvCKEuCmEuCOE+J/KPZ6D\nSAjxx0KIBSHElXKP5SATQnQJIX4ghLi2er34l6UegwrE5fE6cEJKeQq4BfxWmcdzUF0B/hHwVrkH\ncpAIIXTgD4BPAceBXxZCqBJXxfcnwCvlHsQhkAH+tZTyOPAU8N+V+vdZBeIykFL+g5Qys/rPd4DO\nco7noJJSXpdS3iz3OA6gJ4E7UsoRKWUK+Drws2Ue04EjpXwL8Jd7HAedlHJWSvnB6n+HgetARynH\noAJx+f0q8J1yD0JRdqADmLzv31OU+MKlKPtBCNELPAK8W8rnVbWm94kQ4g2gNc9dr0kp/3r1a14j\nuyzyZ6Uc20GynfdZURRlK0IID/BN4H+QUoZK+dwqEO8TKeXLm90vhPinwM8AL0l1hmzXtnqflX0x\nDXTd9+/O1dsUpSoJIaxkg/CfSSm/VernV0vTZSCEeAX4N8BnpZSxco9HUXboPHBECNEnhLABvwR8\nu8xjUm3IPpYAAAC8SURBVJRdEUII4P8Brksp/89yjEEF4vL4fcALvC6EuCiE+KNyD+ggEkL8vBBi\nCnga+FshxHfLPaaDYDXR8F8A3yWb2PIXUsqr5R3VwSOE+H+Bt4FhIcSUEOKflXtMB9SzwD8BXly9\nHl8UQny6lANQlbUURVEUpYzUjFhRFEVRykgFYkVRFEUpIxWIFUVRFKWMVCBWFEVRlDJSgVhRFEVR\nykgFYkVRFEUpIxWIFUVRFKWMVCBWFEVRlDL6/wHzPoX/4Yy1JAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 576x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"o231eJaQPi5E","colab_type":"text"},"source":["Great! We received great performances on both our train and test data splits. We're going to use this dataset to show the importance of data quality and quantity."]},{"cell_type":"markdown","metadata":{"id":"pZ3rnGH8PtBu","colab_type":"text"},"source":["# Reduced dataset"]},{"cell_type":"markdown","metadata":{"id":"ONRP3WQgR3zc","colab_type":"text"},"source":["Let's remove some training data near the decision boundary and see how robust the model is now."]},{"cell_type":"markdown","metadata":{"id":"IoiDkq7ClP2Q","colab_type":"text"},"source":["## Data"]},{"cell_type":"markdown","metadata":{"id":"3E8MUvGCK0zW","colab_type":"text"},"source":["### Load data"]},{"cell_type":"code","metadata":{"id":"2mpWQjcqJlRJ","colab_type":"code","colab":{}},"source":["REDUCED_DATA_FILE = 'tumors_reduced.csv'"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Sxd2S63EYtxt","colab_type":"code","colab":{}},"source":["# Load data from GitHub to this notebook's local drive\n","url = \"https://raw.githubusercontent.com/madewithml/basics/master/data/tumors_reduced.csv\"\n","response = urllib.request.urlopen(url)\n","html = response.read()\n","with open(REDUCED_DATA_FILE, 'wb') as fp:\n","    fp.write(html)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sU69PjH3Z4bm","colab_type":"code","outputId":"701ef61f-e8a4-4964-dca1-bf8c9bb5a6d2","executionInfo":{"status":"ok","timestamp":1583944763019,"user_tz":420,"elapsed":424,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# Raw reduced data\n","df_reduced = pd.read_csv(REDUCED_DATA_FILE, header=0)\n","df_reduced.head()"],"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>leukocyte_count</th>\n","      <th>blood_pressure</th>\n","      <th>tumor_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13.472969</td>\n","      <td>15.250393</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>10.805510</td>\n","      <td>14.109676</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>13.834053</td>\n","      <td>15.793920</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>9.572811</td>\n","      <td>17.873286</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7.633667</td>\n","      <td>16.598559</td>\n","      <td>malignant</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   leukocyte_count  blood_pressure tumor_class\n","0        13.472969       15.250393   malignant\n","1        10.805510       14.109676   malignant\n","2        13.834053       15.793920   malignant\n","3         9.572811       17.873286   malignant\n","4         7.633667       16.598559   malignant"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"id":"pWh45mgZqqaO","colab_type":"code","colab":{}},"source":["# Define X and y\n","X = df_reduced[['leukocyte_count', 'blood_pressure']].values\n","y = df_reduced['tumor_class'].values"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1OwgEJSsZ4g5","colab_type":"code","outputId":"5b4e2ef8-279c-4e41-9a3d-6c518a4b11c6","executionInfo":{"status":"ok","timestamp":1583944765190,"user_tz":420,"elapsed":1166,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":279}},"source":["# Plot data\n","colors = {'benign': 'red', 'malignant': 'blue'}\n","plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], s=25, edgecolors='k')\n","plt.xlabel('leukocyte count')\n","plt.ylabel('blood pressure')\n","plt.legend(['malignant ', 'benign'], loc=\"upper right\")\n","plt.show()"],"execution_count":47,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6AvgFIDXAbwHYDdkDxsBnp5mbN++DgUKFIDdbofD4UDRomVx40YLJCU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5YgS3bNmS68yl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\nfolEIilhSMUvkUgkJYz/B52rbhjyBcK9AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"zxjsnyNtK4EO","colab_type":"text"},"source":["### Split data"]},{"cell_type":"code","metadata":{"id":"RerzDWJQbeVz","colab_type":"code","outputId":"861d98fa-ef6b-41e8-ce76-7d0dee709a9c","executionInfo":{"status":"ok","timestamp":1583944766153,"user_tz":420,"elapsed":636,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X, y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n","class_counts = dict(collections.Counter(y_train))\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")\n","print (f\"Classes: {class_counts}\")"],"execution_count":48,"outputs":[{"output_type":"stream","text":["X_train: (520, 2), y_train: (520,)\n","X_val: (92, 2), y_val: (92,)\n","X_test: (108, 2), y_test: (108,)\n","Sample point: [14.4110029  13.14842457] → benign\n","Classes: {'benign': 281, 'malignant': 239}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"tTNpd_IsLO6V","colab_type":"text"},"source":["### Label encoder"]},{"cell_type":"code","metadata":{"id":"GSAMj9h6lLBo","colab_type":"code","colab":{}},"source":["# Encode class labels\n","y_tokenizer = LabelEncoder()\n","y_tokenizer = y_tokenizer.fit(y_train)\n","num_classes = len(y_tokenizer.classes_)\n","y_train = y_tokenizer.transform(y_train)\n","y_val = y_tokenizer.transform(y_val)\n","y_test = y_tokenizer.transform(y_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"w-Wvh03BMf09","colab_type":"code","outputId":"8bc214d3-dd2a-4cc6-a3e4-55ac1e875ec6","executionInfo":{"status":"ok","timestamp":1583944767907,"user_tz":420,"elapsed":733,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Class weights\n","counts = collections.Counter(y_train)\n","class_weights = {_class: 1.0/count for _class, count in counts.items()}\n","print (f\"class counts: {counts},\\nclass weights: {class_weights}\")"],"execution_count":50,"outputs":[{"output_type":"stream","text":["class counts: Counter({0: 281, 1: 239}),\n","class weights: {0: 0.0035587188612099642, 1: 0.0041841004184100415}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"7Vk9k9dnLSDg","colab_type":"text"},"source":["### Standardize data"]},{"cell_type":"code","metadata":{"id":"-8FjM7u8llPT","colab_type":"code","colab":{}},"source":["# Standardize inputs using training data\n","X_scaler = StandardScaler().fit(X_train)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"F_EdY0FLlXTc","colab_type":"text"},"source":["## Modeling"]},{"cell_type":"markdown","metadata":{"id":"90RuBAKFLj0X","colab_type":"text"},"source":["### Model"]},{"cell_type":"code","metadata":{"id":"-IZ4YOKtSCRk","colab_type":"code","colab":{}},"source":["# Initialize model\n","model = MLP(input_dim=INPUT_DIM, hidden_dim=HIDDEN_DIM, num_classes=NUM_CLASSES)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"J_2ydOvTL39a","colab_type":"text"},"source":["### Training"]},{"cell_type":"code","metadata":{"id":"EKo1sfHsLq2B","colab_type":"code","colab":{}},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7NBWLKDISDj8","colab_type":"code","colab":{}},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-CAhJIhEs5va","colab_type":"code","outputId":"24a7f092-d5be-4b6b-bc43-ab86f0a86e16","executionInfo":{"status":"ok","timestamp":1583944774650,"user_tz":420,"elapsed":347,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# Training\n","for epoch in range(NUM_EPOCHS*10):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%10==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":55,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 0.70, accuracy: 49.6\n","Epoch: 10 | loss: 0.54, accuracy: 88.7\n","Epoch: 20 | loss: 0.42, accuracy: 97.3\n","Epoch: 30 | loss: 0.34, accuracy: 99.6\n","Epoch: 40 | loss: 0.27, accuracy: 100.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"7CnVl8OzMFBL","colab_type":"text"},"source":["### Evaluation"]},{"cell_type":"code","metadata":{"id":"uGWbZlhUSFOz","colab_type":"code","outputId":"a0eb8c2e-e1a7-445c-f33c-8bd92e20ae10","executionInfo":{"status":"ok","timestamp":1583944776738,"user_tz":420,"elapsed":516,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Predictions\n","pred_train = model(X_train, apply_softmax=True)\n","pred_test = model(X_test, apply_softmax=True)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = pred_train.max(dim=1)[1]\n","pred_test = pred_test.max(dim=1)[1]\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":56,"outputs":[{"output_type":"stream","text":["sample probability: tensor([0.9436, 0.0564], grad_fn=<SelectBackward>)\n","sample class: 0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"GLJj7eXneK8C","colab_type":"code","outputId":"fe9fcbe4-ec59-4b93-a1a3-265e8ddca79b","executionInfo":{"status":"ok","timestamp":1583944777410,"user_tz":420,"elapsed":240,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Accuracy\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":57,"outputs":[{"output_type":"stream","text":["train acc: 1.00, test acc: 1.00\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DmTCz8OnSFRn","colab_type":"code","outputId":"8b6a6352-da92-4906-cbcd-31657ba9a042","executionInfo":{"status":"ok","timestamp":1583944778784,"user_tz":420,"elapsed":990,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":336}},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(8,5))\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","\n","# Sample point near the decision boundary (same point as before)\n","plt.scatter(mean_leukocyte_count+0.05, mean_blood_pressure-0.05, s=200, \n","            c='b', edgecolor='w', linewidth=2)\n","\n","# Annotate\n","plt.annotate('true: malignant,\\npred: benign',\n","             color='white',\n","             xy=(mean_leukocyte_count, mean_blood_pressure),\n","             xytext=(0.45, 0.60),\n","             textcoords='figure fraction',\n","             fontsize=16,\n","             arrowprops=dict(facecolor='white', shrink=0.1))\n","plt.show()"],"execution_count":58,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAewAAAE/CAYAAACEgPDhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni8OCNBO/V+gCoS3P5gCrzUDQQ19gLNaJS5o067H9B2o5XFmJliS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VK7ZOtDnnDEpihFdP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\nRmlFo+5kJ0vTc3eLs2xwc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VzqAqbLRd3Jzry2rVCIYVBR4893M7QCpwN2CdNbt7RCIIZB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\nioAO2JqmaZpWBHTA1jRN07Qi8P8DhJ4z72kv0dwAAAAASUVORK5CYII=\n","text/plain":["<Figure size 576x360 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"kdP98xnlbvVn","colab_type":"text"},"source":["This is a very scary but highly realistic scenario. Based on our reduced synthetic dataset, we have achieved a model that generalized really well on the test data. But when we ask for the prediction for the same point tested earlier (which we known is malignant), the prediction is now a benign tumor. We would have completely missed the tumor. To mitigate this, we can:\n","1. Get more data around the space we are concerned about\n","2. Consume predictions with caution when they are close to the decision boundary"]},{"cell_type":"markdown","metadata":{"id":"yWzAC39adTwk","colab_type":"text"},"source":["# Takeaway"]},{"cell_type":"markdown","metadata":{"id":"8q3CiF_xF5rY","colab_type":"text"},"source":["Models are not crystal balls. So it's important that before any machine learning, we really look at our data and ask ourselves if it is truly representative for the task we want to solve. The model itself may fit really well and generalize well on your data but if the data is of poor quality to begin with, the model cannot be trusted."]},{"cell_type":"markdown","metadata":{"id":"cR45QpjQdY6N","colab_type":"text"},"source":["Once you are confident that your data is of good quality and quantity, you can finally start thinking about modeling. The type of model you choose depends on many factors, including the task, type of data, complexity required, etc. \n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/basics/10_Data_and_Models/models.png\" width=\"500\">\n","</div>\n","\n","So once you figure out what type of model your task needs, start with simple models and then slowly add complexity. You don’t want to start with neural networks right away because that may not be right model for your data and task. Striking this balance in model complexity is one of the key tasks of your data scientists. **simple models → complex models**\n","\n"]},{"cell_type":"markdown","metadata":{"id":"l1vJqYsfFoWB","colab_type":"text"},"source":["---\n","Share and discover ML projects at <a href=\"https://madewithml.com/\">Made With ML</a>.\n","\n","<div align=\"left\">\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://github.com/madewithml/basics\"><img src=\"https://img.shields.io/github/stars/madewithml/basics.svg?style=social&label=Star\"></a>&nbsp;\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://www.linkedin.com/company/madewithml\"><img src=\"https://img.shields.io/badge/style--5eba00.svg?label=LinkedIn&logo=linkedin&style=social\"></a>&nbsp;\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://twitter.com/madewithml\"><img src=\"https://img.shields.io/twitter/follow/madewithml.svg?label=Follow&style=social\"></a>\n","</div>\n","             "]}]}